Field of the Invention
The present invention relates to a spectral image data processing apparatus and a two-dimensional spectral apparatus for conducting multivariate analysis on spectral image data of a sample.
Description of the Related Art
Biological samples are often observed in a magnified view using, for example, a microscope. A biological sample is created by cutting tissue into thin slices. The created biological sample is substantially colorless transparent and therefore is often dyed using a pigment.
Since many kinds of substances are included in body tissue, it is possible to detect differences in chemical state and composition of these substances by measuring a spectrum (for example, visible light, ultraviolet light, Raman scattering, stimulated Raman scattering, coherent Anti-Stokes Raman scattering, infrared absorption and X-ray) of the biological sample.
Substances included in the body tissue may be detected by a mass spectrometry method which is a method in which substances are ionized and detected. In the mass spectrometry method, ionized substances are separated in accordance with the mass-to-charge ratio, and a spectrum consisting of the mass-to-charge ratio and its detected strength is obtained.
In particular, information about the form of the biological sample and the chemical state and composition of the substances may be acquired by a spectroscopic imaging method without dyeing the biological sample. Image information of the biological sample and a two-dimensional spectral apparatus for measuring a spectrum corresponding to the image information (i.e., spectral image data) are used in the spectroscopic imaging method.
As an analyzing method of the spectrum, multivariate analysis which uses intensity information as a variate with respect to a wavelength range is adopted.
According to the principal component analysis and the independent component analysis which are kinds of multivariate analysis, if the spectrum of each component included in the biological sample are superimposed to form complicated spectra, classification and measurement of the chemical state of the biological sample may be possible.
As the example thereof, Japanese Patent Laid-Open No. 2011-174906 discloses examining form information and composition of a biological sample by conducting principal component analysis of a spectrum about each pixel and obtaining distribution of principal component scores.
Since a biological sample is a non-uniform sample with various forms and composition materials, spectral image data thereof also becomes varied depending on a target pixel.
When it is necessary to distinguish a subtle difference in samples as in a pathological sample, it is necessary to measure, even though locally, the difference as precise as possible.
In this description, a region in a sample in which a subtle difference needs to be distinguished will be referred to as a “region of interest” and the rest of the region will be referred to as a “region of non-interest.”
In the principal component analysis which is an example of multivariate analysis, a principal component score is calculated by applying an eigenvector to a spectrum of each pixel.
In the past, since the eigenvector has been obtained by, for example, a variance-covariance matrix using spectroscopic spectrum data of many pixels, information about many pixels has been included in the eigenvector.
If a principal component score of spectral image data in a region of interest is calculated using such a eigenvector, spectral image data of a region of non-interest has an influence on the calculated principal component score.
That is, the optimum condition in which the distribution of the principal component scores becomes the maximum with respect to the data in the region of interest is not necessarily obtained.
Therefore, there has been a problem of increasing precision in form observation and composition analysis by obtaining the optimum eigenvector when conducting principal component analysis on the spectral image data.